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Analyzing risk perception and social inﬂuence effects on selfrestriction behavior in response to the COVID-19 pandemic in Japan:
First results
Presentation · August 2020
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The University of Tokyo
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The second Bridging Transportation Researchers (BTR) Online Free Conference
11-12 August 2020
http://bridgingtransport.org/

Analyzing risk perception and social influence effects on
self-restriction behavior in response to the COVID-19
pandemic in Japan: First results

Giancarlos ParadyLecturer
The University of Tokyo

Ayako Taniguchi
Professor
Tsukuba University

Kiyoshi Takami
Associate Professor
The University of Tokyo

The context
Key dates regarding COVID-19 activity restriction requests in Japan
DateAction taken

February 26th , 2020

The central government issues a request to cancel or postpone large-scale events

March 2nd , 2020

The central government requests the closure of all elementary, junior high, and highschools across the country

March 26th , 2020

The Tokyo Metropolis and the four adjacent prefectures in the capital region issue a joint
stay-at-home request

April 7th, 2020
(late afternoon)

The central government declares a state of emergency for 7 prefectures across Japan,
including the Tokyo Metropolis, and the prefectures of Chiba, Saitama and KanagawaApril 9th, 2020

Tokyo governor Koike defines which facilities are requested to stop operations during thestate of emergency

April 11th, 2020

The central government requests a reduction of at least 70% of office commuters for theregions targeted by the emergency state declaration

April 16th, 2020

The central government expands the target of the emergency state declaration to all
prefectures
Source: Asahi Shimbun (2020)←Source: BBC (2020) Coronavirus: The world in lockdown in maps and charts.
https://www.bbc.com/news/world-52103747 (Accessed: May 31, 2020)

Trends in travel behavior changes
Percentage change in visit frequency
to different facilities from the 15th ofFebruary to the 17th of April.
The gray vertical lines mark nationalholidays. The red vertical lines mark thefollowing key events (from left to right):
1)

The Tokyo Metropolis and the four
adjacent prefectures in the capitalregion issue a joint stay-at-homerequest2)

The central government declares astate of emergency for 7 prefectures
across Japan, including the GreaterTokyo Area3)

The central government expands
the target of the emergency state
declaration to all prefectures.

Source: Produced by authors using data from:https://www.google.com/covid19/mobility/

Research question

Given that stay-at-home requests are non-enforceable and non-binding,what factors affect the observed self-restriction behavior at the individual level?

Web-survey details
Target region:▪ Northern Kanto (Ibaraki, Gunma, Tochigi)▪ Greater Tokyo Area (Tokyo, Saitama, Chiba, Kanagawa)Sampling:▪ Method: Quota sampling▪ Sample size:
1) First wave: 800
2) Second wave: 722 (Participation rate: 90.2%)▪ Quota criteria:1) Region (Northern Kanto, Greater Tokyo Area)2) Gender (Male, female)3) Age cohort (20s, 30s, 40s, 50s, and 60s and over)
Survey items:▪ Basic socio-demographic characteristics▪ Mobility tools ownership▪ Measures of social anxiety▪ Risk perception▪ Weekly trip frequency by purpose:
1) Before COVID-19 spread (before February 2020) (t0)2) After COVID-19 spread / before emergency declaration (t1)3) After COVID-19 spread / after emergency declaration (t2)
▪ Behavioral changes in response to COVID-19▪ Perception of degree of going-out self-restriction▪ Social expectations regarding going-out self-restriction▪ Subjective well-being

Number of COVID-19 infections by prefectureRegion
All Japan
Northern Kanto

Greater Tokyo Area

Prefecture
IbarakiTochigi
Gunma
Saitama
Chiba
TokyoKanagawa

Wave 1
Apr.1stApr.8thWave 2
Apr.16th

24
14
19
98
164
527
120

4873
77
15
29
209
284
1203
279

119
39
107
479
525
2457
614

Apr.23rd
12,428
151
52
131
714
725
3452
841

Description of key variables: Risk recognition
Risk recognition (Slovic,1987)
We measured risk perception of COVID-19
using three questions related to:1)

Levels of dread:“I think COVID-19 is frightening”

2)

Familiarity:“I know COVID-19 well”

3) Perception of hazard controllability:“I think COVID-19 can be controlled”
Each item was measured using a 5-point
Likert scale.
We also measured perception of otherhazards to evaluate the relative risk
perception of COVID-19.

■ For the behavioral analysis variables were
dummy-codified so that they take value 1 if
the rating was 4 or 5 and 0, otherwise.

Comparison of risk perception of different hazards

Description of key variables: Perception of self-restriction
Perception of degree of going-out selfrestriction:
Measured as a 7-point Likert scale to thequestion:“to what extent do you think you and otherpeople are practicing going-out selfrestriction?”.
Measured for the respondents themselves,as well as for relatives, work- and orschoolmates, neighbours, other friends,
and the general public.
■ For the behavioral analysis, principal
component analysis was used to extract asingle component of perception of degreeof going-out self-restriction of others.
For ease of understanding, these variables
were normalized so that the possible rangeof values falls between 0 and 1.

Perception of degree of going-out self-restriction behavior (original scale)Description of key variables: Changes in travel behavior▪
▪

From t0 to t1, a large share of the sample reported reductionsin activity frequency.
Large share of no change in behavior for commute, grocery
shopping and other shopping.

▪

From t1 to t2 a rebound for shopping activities was observed,
suggesting a sort of adjustment process.

Description of key variables: Changes in travel behavior▪

Share of decreasing trends ranged from 34% for grocery shopping
to 87% for eating out for business.

Modeling behavioral change
Trip frequencies over time
Variable

MeanSDMed.

t0 (before infection spread)

Grocery shopping

2.409

1.774

2

Other shopping

1.140

1.223

1

Eat-out (alone)

0.713

1.237

0

Eat-out (private groups)

0.541

0.843

0.5

Leisure (alone)

0.543

1.032

0

Leisure (groups)

0.562

0.832

0.5

t1 (after spread - before emergency declaration)
Grocery shopping

1.869

1.603

2

Other shopping

0.787

1.083

0.5

Eat-out (alone)

0.327

0.905

0

Eat-out (private groups)

0.227

0.657

0

Leisure (alone)

0.253

0.804

0

Leisure (groups)

0.212

0.597

0Trip frequency:

Probability of self-restriction:▪
▪

Grocery shopping
Other shopping▪
▪

Eating-out (Alone; in groups)
Leisure (Alone; in groups)▪
▪
▪

Pooled OLS model
Random Intercept modelFirst difference model*▪

Pooled MNL model▪ Choices: Go out, stay home▪

Mixed logit model (error component)▪ Choices: Go out, stay home▪

MNL of changes in frequency*▪ Choices: Increase, decrease, nochanget2 (after spread - after emergency declaration)
Grocery shopping

2.038

1.541

2

Other shopping

0.773

1.118

0.5

Eat-out (alone)

0.295

0.962

0

Eat-out (private groups)

0.122

0.568

0

Leisure (alone)

0.184

0.771

0

Leisure (groups)

0.122

0.530

0Covariates: Gender, age, population density (1/sq.km), household size, number of cars inhousehold, number of infections (city level), commuting frequency.
* Only time-changing covariates included.

Modeling behavioral change: Shopping frequency

Elasticities and marginal effects of key variables for shopping frequency models
Measure of effectElasticity of perception ofdegree of self-restriction ofothersMarginal effect of COVID-19
dread

Model

Grocery shopping

Other shopping

Estimate(95% C.I.)

Estimate(95% C.I.)

Pooled model-0.069-0.109 to -0.029-0.084-0.116 to -0.052

Random effect model-0.036-0.087 to 0.014-0.046-0.100 to 0.007

First difference model-0.035-0.067 to -0.004-0.028-0.041 to -0.015

Pooled model

0.100-0.095 to 0.295-0.110-0.288 to 0.067

Random effect model-0.014-0.240 to 0.212-0.089-0.289 to 0.111

First difference model-0.017-0.319 to 0.285-0.083-0.331 to 0.165

Modeling behavioral change: Reductions in discretionary activities

Elasticities and marginal effects of key variables of discrete choice models (1/2)
Model

Pooled binary logit
of self-restriction
behavior

Error component
mixed binary logit
of self-restriction
behavior

Measure of effectEat out (alone)

Eat out (Private)

Leisure (alone)

Leisure (group)

Est.

95% C.I.

Est.

95% C.I.

Est.

95% C.I.

Est.

95% C.I.

Elasticity of perception ofdegree of self-restriction ofothers: stay home

0.169

0.032 to 0.307

0.107

0.015 to 0.200

0.101-0.020 to 0.222

0.120

0.013 to 0.227

Marginal effect of COVID19 dread: stay home

0.107

0.025 to 0.179

0.092

0.028 to 0.145

0.051-0.019 to 0.110

0.076

0.004 to 0.134

Elasticity of perception ofdegree of self-restriction ofothers: stay home

0.155

0.030 to 0.219

0.096

0.009 to 0.139

0.082-0.050 to 0.148

0.114

0.004 to 0.159

Marginal effect of COVID19 dread: stay home

0.099

0.019 to 0.169

0.095

0.029 to 0.148

0.062-0.009 to 0.122

0.048-0.023 to 0.107

Modeling behavioral change: Reductions in discretionary activities

Elasticities and marginal effects of key variables of discrete choice models (2/2)
Eat out (alone)
ModelMNL ofchanges infrequency

Eat out (Private)

Leisure (alone)

Leisure (group)

Measure of effect
Est.

95% C.I.

Est.

95% C.I.

Est.

95% C.I.

Est.

95% C.I.

Elasticity of perception ofdegree of self-restriction ofothers: increase-0.101-0.271 to 0.070-0.120-0.374 to 0.134-0.026-0.219 to 0.167

0.075-0.234 to 0.384

Elasticity of perception ofdegree of self-restriction ofothers: decrease

0.060-0.023 to 0.142

0.083

0.001 to 0.165

0.080-0.005 to 0.165

0.131

0.051 to 0.212

Marginal effect of COVID-19
dread: increase-0.043-0.072 to 0.023-0.009-0.035 to 0.054-0.039-0.067 to 0.025-0.012-0.039 to 0.075

Marginal effect of COVID-19
dread: decrease

0.158

0.027 to 0.277

0.142

0.019 to 0.254

0.157

0.027 to 0.274

0.1200.004 to 0.228

A simulation exercise

Simulation of the effects of perception of degree of self-restriction of others, and COVID-19 dread on going-out self-restriction(“stay home”) choice probability for eating-out and leisure, and comparison between binary logit and mixed logit results
Other covariates are fixed as follows: time period = t1. All continuous variable set to mean values. All categorical variables set to reference categories

A simulation exercise

Simulation of the effects of perception of degree of self-restriction of others, and COVID-19 dread onchanges in activity frequency choice probability for eating-out and leisure
Other covariates are fixed as follows: time period: 1st period (t1-t0) . All continuous variable set to mean values. All categorical variables set to reference categories

Policy implications
In the context of non-binding requests, soft measures such as campaigns to promote a reduction of non-essential travel
might be more effective if they:▪

Properly convey the severity of the threat posed by COVID-19 as well as its coping mechanisms.
➢ Fear arousal, Protection motivation theory▪

Appeal to the group, rather than the individual, emphasizing the behavior (or at least theperception of behavior) of others.
➢ Identifying the reference group

Based on the above…
▪ Persuading agency has the responsibility to guarantee that the fears claimed are legitimate and that coping measureseffective, by relying on sound science and effective science communication.
▪ Too strong a fear appeal, might result in discrimination and prejudice (“self-restriction police”)▪ Need to achieve a difficult balance: Effective threat appraisal while avoiding discrimination and prejudice▪ Sufficient policy support to minimize the costs of coping behavior.

Take away message▪ As a measure of social influence, the perception of degree of going-out self-restriction of others wasconsistently associated with a small reduction in shopping trip frequencies, and non-negligible increases in theprobability of going-out self-restriction for eating-out and leisure purposes.
▪ As a measure of risk perception, COVID-19 dread was also associated with non-negligible increases in theprobability of going-out self-restriction for eating-out and leisure purposes.
▪ In the context of non-binding going-out self-restriction requests, soft measures such as campaigns to promote a
reduction of non-essential travel might be more effective if they (1) properly convey the severity of the threat
posed by COVID-19 as well as its coping mechanisms and (2) Appeal to the group, rather than theindividual, emphasizing the behavior (or at least the perception of behavior) of others.

Thank you!
Contact: gtroncoso@ut.t.u-tokyo.ac.jp
Parady, G., Taniguchi, A., Takami, K. (2020) Travel behavior changes during the COVID-19 pandemic in Japan: Analyzing
the effects of risk perception and social influence on going-out self-restriction. Transportation Research Interdisciplinary
Perspectives (In press) Open AccessView publication stats